7.8 KiB
title, author, date, output
| title | author | date | output |
|---|---|---|---|
| lesson4_problem_set | Dusty P | May 14, 2018 | pdf_document |
Problem 1 Price vs. x
ggplot(aes(x = x, y = price), data = diamonds) +
geom_point()
2. Findings - Price vs. x
There is a general trend towards an increase in price at what appears to be an exponential rate as x increases. But there are a few outliers at x = 0
3. Correlations
with(diamonds, cor.test(price, x))
##
## Pearson's product-moment correlation
##
## data: price and x
## t = 440.16, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8825835 0.8862594
## sample estimates:
## cor
## 0.8844352
with(diamonds, cor.test(price, y))
##
## Pearson's product-moment correlation
##
## data: price and y
## t = 401.14, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8632867 0.8675241
## sample estimates:
## cor
## 0.8654209
with(diamonds, cor.test(price, z))
##
## Pearson's product-moment correlation
##
## data: price and z
## t = 393.6, df = 53938, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8590541 0.8634131
## sample estimates:
## cor
## 0.8612494
What is the Correlation between price and x? 0.88
What is the correlation between price and y? 0.87
What is the correlation between price and z? 0.86
4. Price vs. Depth
ggplot(aes(x = depth, y = price), data = diamonds) +
geom_point()
5. Adjustments - Price vs. depth
ggplot(data = diamonds, aes(x = depth, y = price)) +
geom_point(alpha = 1/100) +
scale_x_continuous(breaks = seq(0, 80, 2))
6. Typical Depth Range
Based on the scatterplot of depth vs. price, most diamonds are between what values of depth? 60 - 64
7. Correlation - Price and Depth
with(diamonds, cor.test(price, depth))
##
## Pearson's product-moment correlation
##
## data: price and depth
## t = -2.473, df = 53938, p-value = 0.0134
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.019084756 -0.002208537
## sample estimates:
## cor
## -0.0106474
What is the correlation of depth vs. price? -0.01
Based on the correlation coefficient woul dyou use depth to predict the price of a diamond? No
Why? Because a lower coefficient inidcates that the two variables are not closely linked.
8. Price vs. Carat
ggplot(aes(x = carat, y = price), data = diamonds) +
geom_point() +
xlim(0, quantile(diamonds$carat, 0.99)) +
ylim(0, quantile(diamonds$price, 0.99))
## Warning: Removed 926 rows containing missing values (geom_point).
9. Price vs. Volume
diamonds$volume = (diamonds$x * diamonds$y * diamonds$z)
ggplot(aes(x = volume, y = price), data = diamonds) +
geom_point()
10. Findings - Price vs. Volume
What are your observations from the price vs. volume scatterplot? There are some major outliers on the volume scale. Other than that the trend at least appears to be exponential price increase as volume increases.
11. Correlations on Subsets
What's the correlation of price and volume? Exclude diamonds that have a volume of 0 or that are greater than or equal to 800.
with(subset(diamonds, volume != 0 & volume < 800), cor.test(price, volume))
##
## Pearson's product-moment correlation
##
## data: price and volume
## t = 559.19, df = 53915, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9222944 0.9247772
## sample estimates:
## cor
## 0.9235455
12. Adjustments - Price vs. Volume
ggplot(aes(x = volume, y = price), data = subset(diamonds, volume != 0 & volume < 800)) +
geom_point(alpha = 1/20) +
geom_smooth(method = 'lm')
No it is not helpful to look at the linear smooth in this case because it does not fit the data very well.
13. Mean Price by Clarity
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
d_by_clarity <- group_by(diamonds, clarity)
diamondsByClarity <- summarize(
d_by_clarity,
mean_price = mean(price),
median_price = median(price),
min_price = min(price),
max_price = max(price),
n = n()
)
14. Bar Charts of Mean Price
data(diamonds)
library(dplyr)
diamonds_by_clarity <- group_by(diamonds, clarity)
diamonds_mp_by_clarity <- summarise(diamonds_by_clarity, mean_price = mean(price))
diamonds_by_color <- group_by(diamonds, color)
diamonds_mp_by_color <- summarise(diamonds_by_color, mean_price = mean(price))
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
p1 <- ggplot(aes(x = clarity, y = mean_price), data = diamonds_mp_by_clarity) +
geom_bar(stat = "identity")
p2 <- ggplot(aes(x = color, y = mean_price), data = diamonds_mp_by_color) +
geom_bar(stat = "identity")
grid.arrange(p1, p2)
15. Trends in Mean Price
What do you notice in each of the bar charts for mean price by clarity and mean price by color?
In the clarity chart there is a downward trend from SI2 to WS1 but both of the end clarities. (I1 is lower than SI2 and IF is higher than WS1) In the color chart there is a gradual upwards trend from D to J with a slight dip at E.
16. Gapminder Revisited
The Gapminder website contains over 500 data sets with information about the world's population. Your task is to continue the investigation you did at the end of Problem Set 3 or you can start fresh and choose a different data set from Gapminder.
If you’re feeling adventurous or want to try some data munging see if you can find a data set or scrape one from the web.
In your investigation, examine pairs of variable and create 2-5 plots that make use of the techniques from Lesson 4.
You can find a link to the Gapminder website in the Instructor Notes.
data <- read.csv('indicator gapminder under5mortality.csv')
fertility <- read.csv('total_fertility.csv')
library(tidyr)
library(gridExtra)
library(reshape)
##
## Attaching package: 'reshape'
## The following objects are masked from 'package:tidyr':
##
## expand, smiths
## The following object is masked from 'package:dplyr':
##
## rename
data <- melt(data, id = ("X"))
data <- cast(data, variable ~ X, mean)
data <- data[1:216,]
#data
fertility <- melt(fertility, id = ("X"))
fertility <- cast(fertility, variable ~ X, mean)
#fertility
us_data <- data.frame(
year = fertility$variable,
fertility = fertility$`United States`,
deaths = data$`United States`
)
p1 <- ggplot(aes(x = fertility, y = deaths), data = us_data) +
geom_point()
p2 <- ggplot(aes(x = fertility, y = deaths), data = us_data) +
geom_point() +
geom_smooth()
grid.arrange(p1, p2)
## `geom_smooth()` using method = 'loess'
